Tài liệu Soft Sensors for Monitoring P2 doc

Virtual Instruments and Soft Sensors 25

actually an early stage of fault detection. On the other hand, at present, fault

detection and diagnosis is performed by means of advanced techniques of

mathematical modeling, signal processing, identification methods, computational

intelligence, approximate reasoning, and many others. The main goals of modern

fault detection and diagnosis systems are to:

x perform early detection of faults in the various components of the system,

possibly providing as much information as possible about the fault which

has occurred (or is occurring), like size, time, location, evaluation of its

effects;

x provide a decision support system for scheduled, preventive, or predictive

maintenance and repair;

x provide a basis for the development of fault-tolerant systems.

Fault detection and diagnosis strategies always exploit some form of redundancy.

This is the capability of having two or more ways to determine some characteristic

properties (variables, parameters, symptoms) of the process, in order to exploit

more information sources for an effective detection and diagnosis action. The main

idea underlying all fault detection strategies is to compare information collected

from the system to be monitored with the corresponding information from a

redundant source. A fault is generally detected if the system and the redundant

source provide two different sets of information. There can be three main kinds of

redundancy: physical redundancy, which consists of physically replicating the

component to be monitored; analytical redundancy, in which the redundant source

is a mathematical model of the component; knowledge redundancy, in which the

redundant source consists of heuristic information about the process. When dealing

with industrial applications, an effective fault detection and diagnosis algorithm

must usually exploit a combination of redundancy sources, rather than a single one.

Sensor validation is a particular kind of fault detection, in which the system to

be monitored is a sensor (or a set of sensors). At a basic level, the aim of sensor

validation is to provide the users of a measurement system (that can be human

operators, measurement databases, other processes, control systems, etc.) with an

evaluation about the reliability of the measurement performed. At a higher level, a

sensor validation system may also provide an estimate of the measurement in the

case in which the actual sensor is out of order. In this framework, soft sensors are a

valuable tool to perform sensor validation. Their usefulness is twofold. First, they

can be exploited as a source of analytical redundancy. They can in fact be

paralleled with actual sensors, and faults can be detected by comparison between

the outputs of actual and soft sensors. Second, they can be exploited to provide an

estimate of the sensor output in the case of sensor fault. Therefore, they can be

used as a back-up device once a fault has been detected.

2.2.5 What-if Analysis

The design process of control systems requires the process behavior to be

described via adequate theoretical/data-driven models that might be able to predict

the system output corresponding to suitable input trends, for a given time span.